Also, let us denote the variance-covariance matrix of \(X\)  by \(\sigma ^2 \Sigma \) , where \( \Sigma \)  is the \(r\times r\)  covariance matrix of \(Z_{i:n}, i=1,\cdots , r\) . In this notation, the marginal best linear unbiased predictor \(\hat{X}_s\)  of \(X_{s:n}\)  has been derived by [1]}, using the results of [2]}, as
\(\hat{X}_s = \hat{\mu } + \hat{\sigma }\alpha _s + \omega ^{\prime }_s\Sigma ^{-1}(X-\hat{\mu }1 - \hat{\sigma }\alpha ),\) 
